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SKILL verified MIT Self-run

Performance Deity

skill-v0idos-performance-deity-performance-deity · by v0idOS

A Claude skill from v0idOS/performance-deity.

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Install

$ agentstack add skill-v0idos-performance-deity-performance-deity

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
4mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

How agent discovery & health will work →
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About

Performance Deity: Hot-Path Optimization

Description

This skill enforces a rigorous, math-driven approach to code optimization. The agent is forbidden from guessing what makes code faster. It must prove it through benchmarking.

Triggers

Activate this skill whenever the user asks to:

  • "optimize" a function, file, or script.
  • "speed up" or "refactor for speed".
  • "benchmark" or "profile" a specific workflow.
  • Or explicitly runs the command /plugin performance-deity:optimize

Core Directives

When activated, you (the agent) MUST follow these exact steps sequentially. Do not skip any steps.

Phase 1: Establish Baseline

  1. Identify the target code the user wants to optimize.
  2. Use the included tools to run a micro-benchmark for the target language:
  • Python: Use tools/benchmark.py ""
  • Node/JS: Use node tools/benchmark.js ""
  • PowerShell: Use tools/Measure-Performance.ps1 -Command ""
  • Bash/Shell: Use bash tools/benchmark.sh ""
  • If the language isn't supported by these tools, write a custom micro-benchmark script that calculates Average and P95.
  1. Execute the benchmark. Ensure there is a "warm up" phase. Record the P95 and Average execution time over at least 100 iterations.
  2. Report the baseline to the user. Do not proceed to Phase 2 until you have verified the benchmark runs successfully.

Phase 2: Algorithmic Analysis

  1. Analyze the Time Complexity (Big-O) of the current implementation.
  2. Analyze the Space Complexity (Memory allocations).
  3. Explicitly identify the bottleneck. State it clearly (e.g., "Nested loops causing O(n^2) scaling", "Unnecessary object creation causing GC pauses", "String concatenation in a tight loop").

Phase 3: Recursive Refactoring

  1. Rewrite the code using a more efficient algorithm or data structure.
  2. High-Priority Techniques:
  • Replace Arrays/Lists with Hash Sets/Dictionaries for lookups (O(N) -> O(1)).
  • Vectorization or batching instead of individual processing.
  • Caching/Memoization of expensive calculations.
  • Reducing garbage collection overhead (zero-allocation patterns, reusing buffers).
  • Bitwise operations where mathematically applicable.
  1. Run the micro-benchmark on your new code.
  2. CRITICAL DIRECTIVE: If the new code is NOT significantly faster than the baseline, you must discard your changes, apologize internally, and try a different approach. Do not present failed optimizations to the user.

Phase 4: Final Proof

  1. Present the final, optimized code to the user.
  2. Output a Performance Report table comparing the:
  • Baseline Execution Time
  • New Execution Time
  • Percentage Improvement (%)
  1. Briefly explain why the new code is faster based on CPU architecture or memory layout.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.